DEM Super-Resolution with EfficientNetV2
Bekir Z Demiray, Muhammed Sit, Ibrahim Demir

TL;DR
This paper introduces an EfficientNetV2-based model that significantly enhances the resolution of Digital Elevation Models (DEMs) by up to 16 times, addressing data scarcity in environmental monitoring.
Contribution
The paper presents a novel application of EfficientNetV2 for DEM super-resolution, achieving high-quality upscaling without extra data.
Findings
Achieves up to 16x resolution enhancement of DEMs
Demonstrates effectiveness of EfficientNetV2 in geospatial data processing
Provides a scalable solution for high-resolution environmental datasets
Abstract
Efficient climate change monitoring and modeling rely on high-quality geospatial and environmental datasets. Due to limitations in technical capabilities or resources, the acquisition of high-quality data for many environmental disciplines is costly. Digital Elevation Model (DEM) datasets are such examples whereas their low-resolution versions are widely available, high-resolution ones are scarce. In an effort to rectify this problem, we propose and assess an EfficientNetV2 based model. The proposed model increases the spatial resolution of DEMs up to 16times without additional information.
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced Vision and Imaging · Cryospheric studies and observations
Methods1x1 Convolution · Depthwise Convolution · Pointwise Convolution · Depthwise Separable Convolution · Batch Normalization · Inverted Residual Block · EfficientNetV2
